{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 感知机算法实验"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第 0 步：感知机算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Perceptron():\n",
    "    '''\n",
    "        This is a simple implementation of Perceptron algorithm\n",
    "    '''\n",
    "    def __init__(self, learning_rate=0.01, epochs=50):\n",
    "        self.learning_rate = learning_rate\n",
    "        self.epochs = epochs\n",
    "\n",
    "    def train(self, X, y):\n",
    "        self.w = np.zeros(1 + X.shape[1])\n",
    "        self.update_times_list = []\n",
    "        self.error_rate_list = []\n",
    "\n",
    "        for _ in range(self.epochs):\n",
    "            update_times = 0\n",
    "            for xi, yi in zip(X, y):\n",
    "                update = self.learning_rate * (yi - self.predict(xi))\n",
    "                self.w[1:] +=  update * xi\n",
    "                self.w[0] +=  update\n",
    "                update_times += int(update != 0.0)\n",
    "            self.update_times_list.append(update_times)\n",
    "            self.error_rate_list.append((y != self.predict(X)).sum()/y.size)\n",
    "        return self\n",
    "\n",
    "    def predict(self, x):\n",
    "        net_input = np.dot(x, self.w[1:]) + self.w[0]\n",
    "        return np.where(net_input >= 0.0, 1, -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第 1 步：加载Iris数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
      "0                  5.1               3.5                1.4               0.2   \n",
      "1                  4.9               3.0                1.4               0.2   \n",
      "2                  4.7               3.2                1.3               0.2   \n",
      "3                  4.6               3.1                1.5               0.2   \n",
      "4                  5.0               3.6                1.4               0.2   \n",
      "..                 ...               ...                ...               ...   \n",
      "145                6.7               3.0                5.2               2.3   \n",
      "146                6.3               2.5                5.0               1.9   \n",
      "147                6.5               3.0                5.2               2.0   \n",
      "148                6.2               3.4                5.4               2.3   \n",
      "149                5.9               3.0                5.1               1.8   \n",
      "\n",
      "     label  \n",
      "0        0  \n",
      "1        0  \n",
      "2        0  \n",
      "3        0  \n",
      "4        0  \n",
      "..     ...  \n",
      "145      2  \n",
      "146      2  \n",
      "147      2  \n",
      "148      2  \n",
      "149      2  \n",
      "\n",
      "[150 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# load dataset\n",
    "iris_dataset = load_iris()\n",
    "df = pd.DataFrame(iris_dataset.data, columns=iris_dataset.feature_names)\n",
    "df['label'] = iris_dataset.target\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第 2 步：构造两维二分类实验数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 实验参数：选择不同的实验数据（线性可分 vs. 线性不可分）\n",
    "exp_type = 'linear inseparable'\n",
    "if exp_type=='linear separable':\n",
    "    exp_classes = ['setosa', 'versicolor']\n",
    "    exp_features = ['sepal length (cm)', 'petal length (cm)']\n",
    "elif exp_type=='linear inseparable':\n",
    "    exp_classes = ['versicolor', 'virginica'] # 'setosa', 'versicolor', 'virginica'\n",
    "    exp_features = ['sepal width (cm)', 'petal width (cm)'] # 'sepal length (cm)', 'petal length (cm)', 'sepal width (cm)', 'petal width (cm)'\n",
    "else:\n",
    "    print('Wrong experiment type!')\n",
    "    sys.exit()\n",
    "\n",
    "assert(len(exp_classes)==2 and set(exp_classes).issubset(set(iris_dataset.target_names)))\n",
    "assert(len(exp_features)==2 and set(exp_features).issubset(set(iris_dataset.feature_names)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert class and feature names into sample and feature indexes\n",
    "class_id_set = [idx for idx in range(len(iris_dataset.target_names)) if iris_dataset.target_names[idx] in exp_classes]\n",
    "sample_id_set = [idx for idx in range(len(iris_dataset.target)) if iris_dataset.target[idx] in class_id_set]\n",
    "feature_id_set = [idx for idx in range(len(iris_dataset.feature_names)) if iris_dataset.feature_names[idx] in exp_features]\n",
    "\n",
    "# classify setosa and versicolor by their sepal length and petal length\n",
    "y = iris_dataset.target[sample_id_set]\n",
    "y = np.where(y == class_id_set[0], -1, 1) \n",
    "X = iris_dataset.data[sample_id_set, :]\n",
    "X = X[:, feature_id_set]\n",
    "\n",
    "# split the dataset into training set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第 3 步：感知机模型训练与测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weights: [-0.8   -0.236  1.   ]\n",
      "Error rates on training data vs. test data :     8.75% vs. 10.00%\n"
     ]
    }
   ],
   "source": [
    "# train Perceptron\n",
    "# 实验参数：选择不同的迭代次数epochs和学习步长eta\n",
    "ppn = Perceptron(epochs=100, learning_rate=0.01)\n",
    "ppn.train(X_train, y_train)\n",
    "print('Weights: %s' % ppn.w)\n",
    "\n",
    "error_rate_on_training_set = (y_train != ppn.predict(X_train)).sum()/y_train.size\n",
    "error_rate_on_test_set = (y_test != ppn.predict(X_test)).sum()/y_test.size\n",
    "print('Error rates on training data vs. test data : \\\n",
    "    {:.2%} vs. {:.2%}'.format(error_rate_on_training_set, error_rate_on_test_set))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第 4 步： 结果输出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw results\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.suptitle('Perceptron algorithm')\n",
    "\n",
    "# subfigure 1: training samples and learned linear classifier\n",
    "plt.subplot(2,2,1)\n",
    "plt.title('Classifer on training data')\n",
    "b = -ppn.w[0]/ppn.w[2]\n",
    "a = -ppn.w[1]/ppn.w[2]\n",
    "x = np.linspace(X_train[:,0].min(), X_train[:,0].max())\n",
    "f = lambda x : a*x + b\n",
    "plt.plot(x, f(x),'r')\n",
    "\n",
    "plt.scatter(X_train[y_train==-1][:,0], X_train[y_train==-1][:,1], label='-1 : %s' % exp_classes[0])\n",
    "plt.scatter(X_train[y_train==1][:,0], X_train[y_train==1][:,1], label=' 1 : %s' % exp_classes[1])\n",
    "plt.xlabel(exp_features[0])\n",
    "plt.ylabel(exp_features[1])\n",
    "plt.legend()\n",
    "\n",
    "# subfigure 2: weight update times in the training process\n",
    "plt.subplot(2,2,2)\n",
    "plt.title('Weight update times')\n",
    "plt.plot(range(1, len(ppn.update_times_list)+1), ppn.update_times_list, marker='o', label='error times')\n",
    "plt.xlabel('Iterations')\n",
    "plt.ylabel('Update times')\n",
    "\n",
    "# subfigure 3: change of error rate in the training process\n",
    "plt.subplot(2,2,3)\n",
    "plt.title('Error rate on training data')\n",
    "plt.plot(range(1, len(ppn.error_rate_list)+1), ppn.error_rate_list, marker='+', color='coral', label='error rate')\n",
    "plt.xlabel('Iterations')\n",
    "plt.ylabel('Error rates')\n",
    "\n",
    "# subfigure 4: test samples and learned linear classifier\n",
    "plt.subplot(2,2,4)\n",
    "plt.title('Classifer on test data')\n",
    "x = np.linspace(X_test[:,0].min(), X_test[:,0].max())\n",
    "plt.plot(x, f(x),'r')\n",
    "\n",
    "plt.scatter(X_test[y_test==-1][:,0], X_test[y_test==-1][:,1], label='-1 : %s' % exp_classes[0])\n",
    "plt.scatter(X_test[y_test==1][:,0], X_test[y_test==1][:,1], label=' 1 : %s' % exp_classes[1])\n",
    "plt.xlabel(exp_features[0])\n",
    "plt.ylabel(exp_features[1])\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
